# -*- encoding: utf-8 -*-
# author:lmolhw
# datetime:2021-6-10 10:40

"""
文件说明：联合CTC和Attention共同训练，Model文件联合构建

"""
import torch.nn as nn
import torch
from modules.transformation import TPS_SpatialTransformerNetwork
from modules.feature_extraction import VGG_FeatureExtractor, RCNN_FeatureExtractor, ResNet_FeatureExtractor
from modules.sequence_modeling import BidirectionalLSTM
from modules.prediction import Attention
from config import ConfigOpt

class Model(nn.Module):

    def __init__(self, opt):
        super(Model, self).__init__()
        self.opt = opt
        self.stages = {'Trans': opt.Transformation, 'Feat': opt.FeatureExtraction,
                       'Seq': opt.SequenceModeling, 'Pred': "CTC-Attn"}

        """ Transformation """
        if opt.Transformation == 'TPS':
            self.Transformation = TPS_SpatialTransformerNetwork(
                F=opt.num_fiducial, I_size=(opt.imgH, opt.imgW), I_r_size=(opt.imgH, opt.imgW), I_channel_num=opt.input_channel)
        else:
            print('No Transformation module specified')

        """ FeatureExtraction """
        if opt.FeatureExtraction == 'VGG':
            self.FeatureExtraction = VGG_FeatureExtractor(opt.input_channel, opt.output_channel)
        elif opt.FeatureExtraction == 'RCNN':
            self.FeatureExtraction = RCNN_FeatureExtractor(opt.input_channel, opt.output_channel)
        elif opt.FeatureExtraction == 'ResNet':
            self.FeatureExtraction = ResNet_FeatureExtractor(opt.input_channel, opt.output_channel)
        else:
            raise Exception('No FeatureExtraction module specified')
        self.FeatureExtraction_output = opt.output_channel  # int(imgH/16-1) * 512
        self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d((None, 1))  # Transform final (imgH/16-1) -> 1

        """ Sequence modeling"""
        if opt.SequenceModeling == 'BiLSTM':
            self.SequenceModeling = nn.Sequential(
                BidirectionalLSTM(self.FeatureExtraction_output, opt.hidden_size, opt.hidden_size),
                BidirectionalLSTM(opt.hidden_size, opt.hidden_size, opt.hidden_size))
            self.SequenceModeling_output = opt.hidden_size
        else:
            print('No SequenceModeling module specified')
            self.SequenceModeling_output = self.FeatureExtraction_output

        """ Prediction """
        if opt.mtl:
            self.CTC_Prediction = nn.Linear(self.SequenceModeling_output, opt.ctc_num_class)
            self.Attn_Prediction = Attention(self.SequenceModeling_output, opt.hidden_size, opt.num_class)
        else:
            raise Exception('Prediction is not Joint CTC-Attention')

    def forward(self, input, text, is_train=True):
        """ Transformation stage """
        if not self.stages['Trans'] == "None":
            input = self.Transformation(input)
        """ Feature extraction stage """
        visual_feature = self.FeatureExtraction(input)
        visual_feature = self.AdaptiveAvgPool(visual_feature.permute(0, 3, 1, 2))  # [b, c, h, w] -> [b, w, c, h]
        visual_feature = visual_feature.squeeze(3)

        """ Sequence modeling stage """
        if self.stages['Seq'] == 'BiLSTM':
            contextual_feature = self.SequenceModeling(visual_feature)
        else:
            contextual_feature = visual_feature  # for convenience. this is NOT contextually modeled by BiLSTM

        """ Prediction stage """
        if self.stages['Pred'] == 'CTC-Attn':
            ctc_prediction = self.CTC_Prediction(contextual_feature.contiguous())
            attn_prediction = self.Attn_Prediction(contextual_feature.contiguous(), text, is_train,
                                                   batch_max_length=self.opt.batch_max_length)

        return ctc_prediction, attn_prediction

def numel(model):
    return sum(p.numel() for p in model.parameters())


if __name__ == "__main__":
    opt = ConfigOpt()
    opt.imgW = 640
    opt.FeatureExtraction = 'ResNet'
    opt.SequenceModeling = 'BiLSTM'
    opt.Prediction = 'CTC-Attn'
    opt.batch_max_length = 36
    opt.output_channel = 256
    opt.hidden_size = 256
    opt.SRN_PAD = len(opt.character) - 1
    # opt.position_dim = 26
    opt.rgb = None
    opt.alphabet_size = len(opt.character)
    x = torch.randn(1, 1, 32, 640)
    print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel,
          opt.output_channel, opt.hidden_size, opt.num_class, opt.batch_max_length,
          opt.Transformation, opt.FeatureExtraction, opt.SequenceModeling, opt.Prediction)
    model = Model(opt)
    print(model)

    num_params = numel(model)
    print('Model params: {:4f}M'.format(num_params * 4 / 1024 / 1024))
    model = model(x)
    print(model)